Automated parametric modular system or digital platform for forward-looking measurements and ratings of measurable impacts of occurring flood events and modular automated risk-transfer structure providing an adjustable flood impact cover and method thereof

ABSTRACT

The invention relates to automated method and flood measuring and trigger system for providing a dynamic parametric flood impact cover for physical objects being measurably impacted by an occurrence of a flood event by using an adaptive damage-cover structure of a damage-cover system based on physical flood event measurements, wherein by flood detection devices and/or flood sensors, an occurrence of a flood event is measured by measuring floodings within the grid cells of the spatial grid, wherein flood measuring parameters of the flood detection devices and/or flood sensors are transmitted to the central measuring engine and wherein, based on the transmitted flood measuring parameters, grid cells measured as flooded are contributing to the area measured as affected while grid cells measured as not flooded are contributing to the area measured as not affected.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation application of InternationalPatent Application No. PCT/EP2022/080593, filed Nov. 2, 2022, which isbased upon and claims the benefits of priority to Swiss Application No.070500/2021, filed Nov. 3, 2021. The entire contents of all of the aboveapplications are incorporated herein by reference.

FIELD OF THE INVENTION

The field of the invention is directed towards automated systemsproviding parametric flood event impact cover to one or more objectsbased on forward-looking and/or predictive measurements of occurrencesand occurrence rates of measurable physical impacts of catastrophicevents, in particular measurably impacting flood events. Themeasurements related to measurands depending on the physical,location-dependent event strength, location (as geographic area-based,cell-based, event strength line based, or geographic or topographiccoordinate based (as latitude and longitude)), and measured time window,in particular to measurable impacts associated with the occurrence offlood events. Further, the invention is directed to automated parametricmitigation or transfer of the measured forward-looking impact to aspecific object by an automated risk-transfer or risk-absorption system,where the impact e.g. is measured in units of expected damage rate,percentage or other quantifying and/or measuring units associated withthe measured forward-looking impact the specific object. This inventionfurther relates to automated methods and systems for automatedlocation-dependent recognition of flood occurrence probabilities(denoted as flood risks or flood hazards), where flood states areautomatically measured or captured, and location-dependentforward-looking probability values for flood hazards are automaticallyforecasted (e.g. by stochastic structures or machine-learningstructures), measured or generated based on the direct measuring link tothe physical environment. Finally, the invention relates to digital,modular platforms for automated mitigation of impacted physical damagesto physical objects on a certain geographic location and future timewindow.

BACKGROUND OF THE INVENTION

Today reliable automated flood impact and impact response or mitigationsystems are painfully lacking. For many countries, it is hardly possibleto do a technically correct flood impact occurrence rating and/ordetermination based on predictive forward-looking impact measures. Aglance at the loss history shows that physical damages and associatedlosses caused by flood events are equally high or higher than those ofother natural catastrophic events as earthquakes, windstorms, or otherperils. For many of those other perils various prediction and/or ratingand/or early warning systems based on actual measuring parameter valuesalready exist. Large physical part of industrial facilities, industrialpower and time are lost by occurring flood events having a physicalimpact to such objects. Additionally, with the trend of increasingrisk-transfer penetration for floods, the insurance and re-insuranceindustry is affected ever more by hardly to measure and predict floodevents causing physical damages and losses. To extend the early warningcapabilities and flood damage cover, however, the threat of immense dataamounts has to be coped with. One new approach is the present inventivemeasuring system, inter alia, allowing to extrapolate the actualphysical measuring parameters to future time windows and geographiccells.

Further, in many countries, a large number of industrial facilities andhomes have a significant and measurably predictable probability (risk)for being impacted by flood events, and reasonably should be covered byflood mitigation and risk-transfer processes. However, many prior artsystems are not capable to reliably hedge against the technicallydifficult to predict perils of flood events, inter alia, due to theprevalence of moral hazard and adverse selection phenomena, for example,in entering risk-transfers for objects most affected by the specificperil of flood. In such cases, traditional risk-transfer is notavailable. Whereas for other damage risks, risk-transfer systems can bebased on the use of the law of large numbers to precisely determine arelatively small premium amount to large numbers of objects in order tocover the occurring damages of the small numbers of impacted objects whohave suffered a loss due to the event-based impact to their objects. Inflood event covers, typically the numbers of impacted objects is largerthan the available number of individuals interested in protecting theirproperty/objects from the peril using risk covers, which means that mostprior art insurance systems do not provide risk-transfers to occurringflood events since the probability of operating the system in a soundprofit range are regarded as being remote. Additionally, while there arerisk-transfer systems that are enabled to provide primary flood riskcovers for high value homes, the underwriting and provision of suchmitigation processes does not account for many flood risks.

The lack of flood reliably automatable risk mitigation, risk-transferand risk covering systems can be detrimental to the operation of manyindustrial facilities at a certain location or detrimental to localhomeowners of the geographic cell concerned. Even, in many cases, theresponsible may discover only after an occurring event impacting adamage that their standard damage covers, and risk-transfers do notcover damages caused by flooding. It is to be mentioned that floodingcan occur due to or in the wake of various physical natural disasters,for example occurring earthquakes, landslides, tsunamis, volcaniceruptions, hurricanes, cyclones, storm surges, glacial melting, or othernatural disasters. Furthermore, few risk-transfer systems provide flooddamage coverage due to the hazard of flood typically being confined to afew areas. As a result, it is an unacceptable risk due to the inabilityto spread the risk to a wide enough group of objects in order totechnically absorb the potential catastrophic nature of the hazard.

In summary, natural disasters such as floods cause severe damage invarious parts of the world. The occurrence of most of such disasterevents is difficult, if not impossible, to predict over the long term byprior art measuring systems. Conventional flood mitigation techniquesdetermine, assess and estimate pricing of flood risks using parametricrisk-transfer structures so as to mitigate flood risks by gatheringinformation related to base flood elevation data, flood depth by usingmitigation devices and survey information. In addition, digitalmarketplace techniques list online policies that guarantee a pre-agreedpayout based on pre-determined parameters in case of a flood hazard. Thedigital marketplace techniques determine a water-elevation functionfactoring in high water probability data, leading to saving bothrisk-transfer providers' and customers' time and cost spent on building,inspection, and damage estimation. Additional mechanisms for assessingflood risks use measured flood levels through detection chambers thatassess floods based on a determined flood level measured by air-basedpressure measuring devices and/or remote sensing techniques, as e.g.satellite based determination of floods. Further, the measured floodlevels are used to automate signaling and triggering of cover for aphysical impact, typically measured as loss or damage at the impactedobject, for example by a flood damage cover payout.

Other available risk transfer mechanisms capture event data of occurredflood and maps data to a digital map along with data related to risktransfer of portfolio. The portfolio is mapped by geographic area andvalue so that an exposure and risk to the portfolio induced by a floodevent is determined. Yet another mechanism for assessing flood risks bysimulating movement of water in case of floods is to determine potentialfor water damage to surface of a structure. This mechanism providesautomated expert advices relating to whether to apply floodrisk-transfer/insurance for risk-exposed structure based on simulationof water movement in relation to the structure.

Parametric insurance/cover is a type of risk-transfer that is providedto the individual based on one or more pre-agreed measure (the measuringor triggering “parameter” or “index”). The parametric risk cover islevelled by an adjustable degree or threshold value of a predicted riskmeasure based on, for example, geographic location, exposure thresholdetc. Such values can e.g. be generated using prior art hazard mappingapplications such as CatNet. The exemplary, proprietary hazard mappingapplication CatNet is a global, web-based natural hazard analysis andmapping software-based engine, which enables the user to assess andvisualize natural hazard exposure for a certain location in the world.Such prior art data-processing engines, as data-link to the application,typically comprise Application Programming Interface (API) providingaccess to the proprietary risk modeling structures (as e.g. Cat ServerAPI) or any probabilistic flood modeling techniques.

Based on the degree of risk accessed, a parametric expertise may bedetermined to tailor a product design. This may involve introducingindex definition including double trigger functionalities, quoting, andpricing, combining parametric and indemnity risk-transfer. Further,parametric transfer structures may be determined using naturalcatastrophe (NatCaT) pricing tool for parametric damage and damageexposure cover. In addition, based on the inventive parametricstructure, a modular computer-supported platform can be provided thatprovides an end-to-end solution from automated front-end andunderwriting to automated natural catastrophe impact covering, e.g. byautomated electronic monetary transfer. The platform may be used asfront-end for users, for event tracking, for automated activation orelectronic triggering of electronic alarm devices e.g. based onforecasted event progressing, measured and/or forecasted event impactand/or physical damage parameters, maintaining proof of loss, generatingautomatic claims payment, and sustaining policy administration.

The above-disclosed mechanisms/techniques do not discuss about providinga modular system that determines, assess and evaluate price risks thatarise due to occurrence of floods. In addition, thesemechanisms/techniques fail to provide parametric risk transferstructures that facilitate to mitigate the risks associated withoccurrence of the floods. There is therefore a need in the art for animproved, automatable system and method for providing a customizableimpact cover structure for a flood area risk based on measurable andreliable prediction and assessment of location- andtime-window-dependent physical impact damage during the occurrence offlood events.

SUMMARY OF THE INVENTION

It is one object of the present invention to provide an automated systemand method for reliable forward-looking measurements and ratings ofphysical impacts of occurring location-dependent catastrophic events, asflood events, to a specific object, and in automated conduct andprovision/generation of appropriate covers and/or hedging against theimpacted damage to the specific object. It is further an object of thisinvention to provide a new and better automated system and method forproviding a dynamic parametric cover, which does not have theabove-mentioned disadvantages of the prior art. In particular, it is anobject of the present invention to provide a risk-transfer cover basedon an extend of a flooded area. Further it is an object, to generate apricing measure for a cover based on the chosen limit value (payoutcover) and a measured and/or forecasted exposure value (e.g. related tothe amount severity and/or extend of flooding to an area). The extent offlooded area is generated by assessing an Area of Interest (AOI) usingautomated systems such as Synthetic Aperture Radar or Drones and bydividing the area into grid cells. The AOI can e.g. be defined client-or user-specific. Ideally the grid cells cover the entire AOL Later,with using technology such as SAR, it can e.g. be determined how much ofthe AOI is flooded (in percent area or number of grid cells)

The size of the grid cells can e.g. be predefined and can be differentfor different risk-transfer structures and user to be covered. The gridsize can, thus, be predefined or automatically negotiated between a userand the inventive system, or it can by automatically and/or dynamicallyadjusted by the system based on a desired resolution within a selectedarea. The resolution can also be varied by the system e.g. in dependenceto the severity of the impact in a certain area, e.g. the severity offlooding. The resolution can also be increased automatically and/ordynamically at the border of the affected area to achieve a more precisequantitative measure for the actual affected or impacted area.

In particular, these aims are achieved by the inventive flood measuringand trigger system for triggering a dynamic parametric flood impactcover for physical objects being measurably impacted by an occurrence ofa flood event by using an adaptive damage-cover structure of anautomated electronic damage-cover system based on physical flood eventmeasurements, in particular in that the flood measuring and triggersystem comprises a central measuring engine with a predefined datastructure for capturing a geographic area to be covered, the datastructure at least comprising definable area parameters capturinggeographic location and/or geographic extent of said geographic area, inthat predefined data structure comprises grid parameters for splittingthe geographic area to be covered by the spatial grid with equidistantgrid cells of definable size, in that the flood measuring and triggersystem comprises flood detection devices and/or flood sensors formeasuring an occurrence of a flood event by measuring floodings withingrid cells of the spatial grid, wherein flood measuring parameters ofthe flood detection devices and/or flood sensors are transmitted to thecentral measuring engine and wherein, based on the transmitted floodmeasuring parameters, grid cells measured as flooded are contributing tothe area measured as affected while grid cells measured as not floodedare not contributing to the area measured as affected, in that thecentral measuring engine comprises a flood threshold measurand, whereinthe flood threshold measurand is selected from a percentage of thegeographic area given by the area measured as affected to the geographicarea, and in that the central measuring engine comprises an electronicflood trigger for triggering the adaptive damage-cover structurecovering physical damages or loss associated with the measuredoccurrence of the flood event, the occurrence measurably impactingphysical objects situated in the affected area and/or grid cellsmeasured as affected, wherein the adaptive damage-cover structure isadapted by the damage-cover system based on the flood thresholdmeasurand providing the dynamic parametric flood impact cover.

In another embodiment of the invention, the grid cells are measured asflooded when each grid cell of the measured affected area is flooded.The grid cells are defined as two dimensional m×n blocks. The m×n blockscan e.g. be of approx. 8.72*10−5 radian or 0.005×0.005 deg allowing agood resolution of flood measurements. Flood determination per grid cellcan e.g. be binary at the centroid. However, it can also be determineddependent on the flood depth, i.e. more refined.

The grid cells measured as flooded can e.g. be determined using anartificial intelligence-based or machine-learning based engine, such asa neural network data processing structure, and the affected area cane.g. be measured using on-air imaging devices. The variant with theproposed grid-cell-based floodplain mapping is an inventive remotesensing intensive process that can e.g. be implemented all over thedefined area. It can e.g. comprise a Digital Elevation Modelling (DEM)structure, with the predefined or adaptive measuring accuracy based onthe selected grid size. The DEM structures allow the inventive system toderive the slope and terrain characteristics of the selected area. Isthere a river, and if so is there a flat expanse of plain around thatriver? What might be below sea level that is near the mouth of a delta?To get the highest accuracy possible for these DEMs, the inventivesystem can e.g. use remote sensing. In particular measuring devices foraerial or space-based photography and/or LiDAR devices and SyntheticAperture Radar (SAR) devices to produce the required high-accuracy DEMs.Aerial imagery can be used with multiple images to generate a DEM beingcaptured using e.g. a plane or UAS. However, the quality of aerialimagery can e.g. be affected by lighting conditions and clouds. LiDAR,which shoots pulses of light and records their response, is an activesystem. That means it is less affected by lighting conditions, but canmeasure through light haze and clouds. Also SAR is able to measurethrough clouds at any time day or night, but is one of the mostcomplicated remote sensing systems to be integrated. With the inventiveDEMs, which can also be improved by other imagery sources such assatellite, or UAS imagery, features of importance can be monitoredand/or detected in relation to how they relate to possible floodplains.Using remote sensing, the present system allows to classify land coverinto many different areas of interest, such as low-lying plains,forests, sandy deltas, and urban areas, and then use thoseclassifications to feed into the floodplain modeling structure. WithLiDAR, the system can e.g. also automatically identify buildings anddetect where they lie within the possible floodplains, i.e. assignfloodplain-related measuring parameter values. These multiple inputscreate a map that shows areas where flooding is measured with a higherprobability value. The automated mapping can also be used for predictivemodelling, cover or flood impact mitigation, and flood preparation orautomated alarm signaling for activation of automated alarm systems.

As an embodiment variant, the system is automated to electronicallydisperse payments or transfer monetary parameter values to achieve thedesired automatic cover covering the physical damage or impact caused bythe natural event, e.g. the flooding event. The payments are dispersedvia an electronic payment transfer module based on the generatedparametric coverage monetary pay-out parameter. In particular, it is anobject of the present invention to provide an automated method andsystem for providing dynamic parametric cover to an individual in caseof an occurrence of a flood event by using an adaptive risk-transferstructure based on physical flood event measurements.

In another embodiment of the invention, wherein a premium vale isautomatically generated based on the selected payout coverage and theforecasted exposure measure, i.e. the forecasted probability measurandfor a future time window based on the measured occurrence frequenciesand strength. Each risk-transfer can be parameterized as an exchange ofrisk to cover an actual physical impact to an object for a typicallymonetary transfer. Each impact by a risk event can be realized by aclaim equivalent value, which represents the variable cost factors of arisk-transfer system. It is to be noted that the variable cost factor isdifficult to determine compared to variable cost factors in otherfields. For example, in the case of a production line, the variablecosts, such as raw materials, are quite certain, which makes it easierto minimize it. In technical field of risk-transfer, on the other hand,variable cost factors are often measured to be a probabilisticdistribution. Therefore, it is technically challenging to minimize it.In the present invention, Al/ML data processing structures are used toprocess the flood measuring data. In a simplified embodiment variant,also linear regression and/or generalized linear model (GLM) could beused for processing the measuring data and forecast the appropriateprediction parameter value for the future exposure measurands. However,the inventive AI-based underwriting measures risk measurands (i.e. theforecasted exposure probability values) with a much higher precision. Inaddition, the measuring data of the inventive system are rich comparedto prior art system, as it is linked to and comprises a plurality ofIoT-measuring devices. Therefore, the inventive system is able to assessthe forecasted risk measurands more precisely. Uncertainty of variablecost values diminishes for the inventive system adopting the proposedtechnologies. Thus, the system makes it easy to determine and optimizeautomatically and self-sufficiently pricing values for the risk-transferbased on the measured flood parameter values. Further, the geographicarea is of unvarying landscape representative of area covered by dryland and wetland.

Further, the occurrence of the flood event can e.g. be detected usingloopback signaling. The herein proposed technical loopback mechanism isespecially shaped for the flood detection providing an inventiveloopback flood detection method and system. A source maintenance endpoint at the flood area sends a loopback message to the targetmaintenance point at the central measuring engine, wherein the loopbackmessage comprises a flow identity corresponding to one specifictransmission path in a plurality of equal transmission paths to thetarget maintenance point. After receiving the loopback message, thetarget maintenance point sends a loopback replay message to the sourcemaintenance end point, wherein the loopback replay message comprises aflow identity corresponding to a reverse common transmission path to thesource maintenance end point. After receiving the loopback replaymessage and detecting to be correct, the source maintenance end pointreturns an announce of successful loopback detection. According to theinventive loopback detection method, the working mechanism of prior artloopback detection is expanded, and loopback detection can be performedon one specific path in a plurality of equal paths, thus allowing andproviding high-speed transmission and detection of occurring flood byflood measurements in real-time or quasi-real-time.

According to the present invention, these objects are achievedparticularly through the features of the independent claims. Additionalfeatures and advantages will become apparent to those skilled in the artupon consideration of the following detailed description of illustrativeembodiments exemplifying the best mode of carrying out the method aspresently perceived.

SHORT DESCRIPTION OF THE FIGURES

The present disclosure will be described hereafter with reference to theattached drawings, which are given as non-limiting examples only, inwhich:

FIG. 1 is a block diagram illustrating exemplary an architecture of theinventive flood measuring and trigger system 1 and method for triggeringa dynamic parametric flood impact cover for physical objects 3/31 beingmeasurably impacted by an occurrence of a flood event 4 by using anadaptive damage-cover structure 1031 of an electronic damage-signalingand/or damage-cover system 103 based on physical flood eventmeasurements.

FIG. 2 is a schematic diagram, which exemplarily illustrates a possibleoverall operation of one embodiment of the method of the presentinvention.

FIG. 3 is a chart, which shows a structure scheme presenting an impactof flood event.

FIG. 4 is a chart, which shows a payout scheme used during occurrence offlood event in relation to topography and exposure distribution.

FIG. 5 schematically illustrates an exemplary parametric risk-transferrelying on the measurement of a natural physical measuring parameter orindex (parameter). Automated cover by a payout transfer given by apredefined amount is made when a predefined threshold of parameter/indexis exceeded (e.g. amount of rainfall, flood at location).

FIG. 6 schematically illustrates an exemplary parametric excess rainfallrisk-transfer (as a proxy for flood). Daily average rainfall amountsover Central America measured by satellite measuring data. An aggregatedamount of rolling observation window (e.g. 2-days) must exceedpre-defined rainfall levels. It is to be noted that rainfall≠floods onthe ground.

FIG. 7 schematically illustrates an exemplary flood map generationthrough multi-source measuring parameter processing.

FIG. 8 schematically illustrates an exemplary processing of flood eventfootprints, where satellite data are measured, an object level of a highspatial resolution up to 1×1 meter is achieved, daily observations withany non-permanent water for >12 hours is detected and delivered nearreal time, and radar technology allows monitoring through clouds and atnight.

FIG. 9 schematically illustrates an exemplary parametric floodrisk-transfer structure triggered by the inventive flood measuringfootprints based on maximum flood extent.

FIG. 10 schematically illustrates an exemplary identification ofcritical sites or objects 3 and generating a grid over a geographic area2 of interest at which a flood is measured. A flood extent mapping isprovided by the system 1 within 24 hours after peak flood. The system 1automatically detects if pre-determined sites were flooded. Cover withdefined payout amounts for all affected sites and/or objects 3 can e.g.be provided within days, thereby optimizing financial resilience andenable an immediate response.

FIG. 11 schematically illustrates an exemplary flood map or digitalelevation map from satellite imagery classification. Physical objects 3,as e.g. buildings or other constructions, can e.g. be automaticallylocated using LiDAR which, inter alia, allows the system 1 to detect howclose a physical object 3 lies or within which flood zones.

FIG. 12 shows schematically a machine-learning setup. The geographicarea 2 is divided in 50×50 grid cell 1003 raster each simulated by itsown machine-learning structure. Input layer: seven input hydrographs.Output layer: flood inundation extent in each grid 1002 and/or grid cell1003.

FIG. 13 shows schematically grid cells 1003 based stream-flowmeasurements which can e.g. be an important measuring parameter thatalso impacts many other aspects as e.g. the river's hydrology and waterquality.

FIG. 14 shows schematically an exemplary flood grid 1002 measuring flooddepth, where the darker areas represent greater flood depths

FIG. 15 shows schematically exemplary dynamically adjusted grid cells1003, wherein the cells 1003 are chosen to be dynamically adapted by thesystem 1, where areas closer to the flooding and/or river etc. areapplied with a high measuring resolution than areas further away. Thisallows a more precise measurement of flooded areas 21 than achieved bythe prior art systems.

FIG. 16 shows schematically exemplary measurements of the geographicarea 2 by satellite measurements detecting flooded grid cells 1003 andnot flooded grid cells 1003. The influence of clouds is eliminated bycomparing and/or calibrating dry and wet measuring signals (leftdiagram). Water has a lower brightness temperature measured than land(right diagram).

FIG. 17 shows schematically exemplary measurements a flood event 4/41,42, 43 in a the geographic area 2 based on grid cell measurements.

DETAILED DESCRIPTION

FIG. 1 shows an exemplary architecture for a flood measuring and triggersystem 1 and an automated method for providing a dynamic parametricflood impact cover 1031 for physical objects 3/31 being measurablyimpacted by an occurrence 41/42/43 of a flood event 4 by using anadaptive damage-cover structure 1041 of a damage-cover system 104 basedon physical flood event measurements.

The flood measuring and trigger system 1 comprises a central measuringengine 10 with a predefined data structure 1005 for capturing ageographic area 2 to be covered, the data structure 1005 at leastcomprising definable area parameters 10041 capturing geographic location100511 and/or geographic extent 100512 of said geographic area 2.Predefined data structure 1005 comprises grid parameters 10033 forsplitting the geographic area 2 to be covered by the spatial grid 1002with equidistant or variable grid cells 1003 of definable orautomatically adaptable size 10033 by means of a dynamic grid splitter1001. The grid cells 1003 can e.g. be defined as two dimensional blocksof m×n size 10033. The two dimensional m×n blocks 1003 can e.g. be ofapprox. 8.72*10−5 radian size 10033. The dimensional m×n blocks 1003 cane.g. be of approx. 0.005*0.005 deg size 10033. Grid shape 10034(curvilinear/structured or triangular/unstructured) and the selectedgrid size 10033 affect the output of the damage signaling or damagecover system 104. For the present system 1, e.g. dependent on thetopology or geographic characteristics or desired resolution,structured, unstructured and/or hybrid grids 1002 can be applied.

To capture flood exposure or risk measuring data and to automaticallytrigger actions or alarm systems, detailed flood measuring parametersvalues 1021 are measured on depth of flooding and/or probability offlooding and/or other flooding characteristics, transmitted to thecentral measuring engine 10 and stored as grid measuring datasets (seeFIG. 14 , where the darker areas represent greater flood depths). Themeasuring grid cells 1003 can e.g. be realized as a digital rasterdataset that defines geographic space as an array of equally oradjustably sized square cells 1003 arranged in rows and columns. Thevalue in each cell represents the magnitude in that location of theflooding characteristic represented by that particular grid. Within aflood measuring parameter database, grids 1002 can be produced toreflect water surface elevations and/or depths and/or velocities and/orpercent-annual-chances of flooding and/or other measuring values. Thegrids 1002 can e.g. be chosen based on the topography of a geographicarea 2, e.g. being unique to coastal areas and dams.

In one embodiment variant, for the grid cell resolution, it should e.g.be taken into account when selecting the cell size 10033 for the grids1002, that the depth and measuring grid cells 1003 have an inherentrelationship to the underlying topographic data used during thedevelopment of the flood hazard delineations, which can e.g. be depictedon the flood risk rate map. The raster cell size (resolution) 10033 ofall raster datasets measured should be based on the density of theground elevation data used and the appropriate precision that can besupported by the measurements. Normally, all the gird measuring datasetscan e.g. use the same grid cell size 10033. However, the cell size 10033for the grids 1002 can e.g. be no larger than 3×3 m. This will allow fora more accurate depiction and retrieval of the measuring values fromthat grid dataset. In the present system 1, a two dimensional horizontalmodeling structure can e.g. be applied to get the detailed and accuratemap of water levels and/or flood patterns and/or potential flood exposedareas. Instead of equal sized grid cells 1003, also structuredcurvilinear grids 1002 can e.g. be used for the hydraulic forecastprocessing, where the curvilinear grid cells 1003 provide a preciseforecast output signaling by allowing cell stretching along river mainchannels while orthogonality stays within reasonable bounds. However,the use of curvilinear grid cells can e.g. result in a high resolutionin sharp inner bends since grid lines are focused on the bends. Atechnically not required high resolution can strongly increasecomputational and processing time. In a preferred embodiment variant,the measuring grid cells 1003 are chosen to be dynamically adapted bythe system 1 (see FIG. 15 ), where areas closer to the flooding show ahigh resolution than areas further away. This allows a more precisemeasurement of flooded areas 21 than achieved by the prior art systems.

The flood measuring and trigger system 1 comprises flood detectiondevices and/or flood sensors 102 for measuring an occurrence of a floodevent 4 by measuring floodings within grid cells 1003 of the spatialgrid 1002, wherein flood measuring parameters 1021 of the flooddetection devices and/or flood sensors 102 are transmitted to thecentral measuring engine 10 and wherein, based on the transmitted floodmeasuring parameters 1021, grid cells 1003 measured as flooded arecontributing to the area 21 measured as affected while grid cellsmeasured as not flooded are not contributing to the area 22 measured asaffected. The geographic area 2 can e.g. comprise surveyed landscaperepresentative of area covered by dry land 24 and wetland 25. Thegeographic area 2 can e.g. comprise at least parts definable asautomatically excluded from the dynamic parametric flood impact cover1031. The occurrence of the flood event 4 can e.g. be detected usingloopback signaling 1022 for the signaling of the flood detection devicesand/or flood sensors 102. The herein proposed technical loopbackmechanism is especially shaped for the flood detection providing aninventive loopback flood detection method and system. A sourcemaintenance end point at the flood area sends a loopback message to thetarget maintenance point at the central measuring engine, wherein theloopback message comprises a flow identity corresponding to one specifictransmission path in a plurality of equal transmission paths to thetarget maintenance point. After receiving the loopback message, thetarget maintenance point sends a loopback replay message to the sourcemaintenance end point, wherein the loopback replay message comprises aflow identity corresponding to a reverse common transmission path to thesource maintenance end point. After receiving the loopback replaymessage and detecting to be correct, the source maintenance end pointreturns an announce of successful loopback detection. According to theinventive loopback detection method, the working mechanism of prior artloopback detection is expanded, and loopback detection can be performedon one specific path in a plurality of equal paths, thus allowing andproviding high-speed transmission and detection of occurring flood byflood measurements in real-time or quasi-real-time.

It is to be noted that the present inventive measuring system 1 can e.g.in an embodiment variant also be used as flood warning system 1.Monitoring is important: While some areas are more exposed to floodingthan others, situating measuring devices and flood sensors 102 nearwaterway or body of water provides critical and precises in-locomeasuring data. The affected area 21 can e.g. be measured using gagesand telemetry equipment 1025. The present inventive system 1 is based onthe regular measuring of local rainfall, stream level, and streamflowdata in each grid cell 1003. This can be real-time monitoring withtelemetry allowing for the fastest possible response to a flood event.It is clear that a real-time flood warning system 12/121, . . . , 123can reduce risks involved with flooding. The affected area 21 can e.g.be measured using at least partially air-based and/or space-basedoptical measuring devices 1023/1024 using digital image recognitionprocessing (see FIG. 16 ). The present system 1 forecasts floods usingthe below described model structures for forecasting how rivers andstreams respond to varying levels of measured rainfall and snowmelt.These forecast processing are based on already measured data records offlood level and/or stream stage and/or discharge, the generation forwhich are outlined below.

There are a broad variety of automated stream gages that can transmitstream level data via telemetry. Gages developed according to the NWSALERT protocol are among the most common. However, it's to be noted thatmany other gages designed to measure precipitation and water leveloperate under similar principles, and this guide may be applicable tocertain aspects of other systems. The gages, used herein, perform twoprimary tasks: sensing and communicating. The gages, as used herein,employs sensors to detect changes to measuring parameters, e.g.precipitation volume and/or water level. As an embodiment variant, theused gages may also be equipped with temperature and wind speed sensors.Some gages can also provide site-specific measuring data regarding thehealth or technical status of the measuring unit. For example, for thepresent inventive system 1, the gage can e.g. be designed to detect aparticular “event”, e.g. 1 millimeter of rainwater entering the gage'stipping bucket through the top of its funnel. When the bucket tips, itpours out any water within, engaging a switch that transmits alert dataand resetting the bucket. Any other sensors on the gage will alsoactivate the alert data transmitter after detecting said specific event.On days without rain, the gages can e.g. transmit a “no rain” report toshow that the device 102 is still working. In the embodiment variantwith automated flood warning 12/121,122,123 the system 1 can e.g. useradio, cellular, or satellite telemetry to communicate with an alarmhost computer or alarm network. The system 1 can also specificallyoperate alarm signals using radio frequencies and/or satellite and/orcellular telemetry. For the present system 1, it is to be noted thatwhile streams and rivers can be monitored for many mearing qualities andparameters that they share with lakes, ponds and basins, streams andrivers possess one quality that differentiates them from otherfreshwater bodies, namely their movement. For the forecast processing bythe present system, stream-flow can e.g. be an important measuringparameter that impacts many other aspects of a river's hydrology andwater quality (see FIG. 13 ).

As an embodiment variant, the grid cells 1003 measured as flooded 10031can e.g. be determined using at least an artificial intelligence baseddata-processing structure and/or a machine-learning-baseddata-processing structure. It is to be noted that the present inventivesystem generally can also be operated applying one or more types ofnumerical modeling structures for predictive flood parameter generation.For example, a hydrological rainfall run-off modeling structure can e.g.be used to forecast distributed river discharges. Also a one-dimensional(1D) drainage modeling structure can e.g. be applied based on theone-dimensional Saint-Venant flow forecasting to predict surcharges ordrainages. Further, also a two-dimensional (2D) Saint-Venant flowforecast can e.g. be used for simulating the surface inundation, andobtain a forecasted maximum flood extents, maximum depths, and flowvelocity on defined points on the surface. Furthermore, a 1D-2D couplingstructure can be applied. All proposed embodiment variant relies onsensory and/or field measurements for capturing the inventive inputparameters and their specific technical selection. However in apreferred embodiment variant, the system relies on a data-drivenapproach for establish a reliable flood forecast structure based onflood measurements. Unlike the numerical structure, the inventivedata-driven, physical-based measuring and forecast system requiresmeasuring input/output data only. The inventive data-driven structurehas shown its high performance especially for the technical nonlinearflood forecast problems. In particular, ANN-based forecast structurescan be applied, however the technical problem of over-fitting orunder-fitting the measuring data, and insufficient length of the datasets can lead to erroneous forecast output results. To expand thedata-driven forecast structures for short and long term flood forecasts,combined use of neuro-fuzzy structures and/or support vector machine(SVM) and/or support vector regression and/or artificial neural network(ANN) are also possible. More particularly, artificial neural networkshowed to be a reliable technique for the inventive flood prediction, asfor forecasting water levels by applying ANN forecast structures toconventional hydrological modeling in flood-prone catchments. In anotherembodiment variant, the water level forecast results along a river usesbackpropagation and/or conjugate gradient and/or cascade correlation.One possibility is to combine a Levenberg-Marquardt Backpropagation withcross-validation to prevent the under-fitting and overfitting in dailyreservoir inflow forecasting.

Finally, in another preferred embodiment variant, the maximum floodinundation in a geographic area is determined by machine-learning basedstructure applying a backpropagation networks based on multiple inflowmeasuring data for a grid resolution of m×n. This ANN technique allowsfor a geographic area to provide high-resolution flood inundation mapsfrom river flooding. For the prediction of maximum flood inundation,only the real-time discharges of the upstream catchments or flood levelmeasurements are needed. The procession consists of two phases: thetraining phase collects a part of the measuring data from the existingdatabase, tuning the model by changing the weights on input arcs tominimize the bias on the output layer; the recalling phase produces thenew outputs for the testing inputs. The rest individuals in the trainingdataset are used for evaluating the behavior of the network structure.The total bias between the output of ANN and the observed values isdefined as the error function. In order to reduce the error function ineach iteration, the weights are modified automatically by the system 1.Herein, the learning rate is used for automatedly scaling the gradientin each iteration of the weight update. It is to be noted that thesystem can be sensitive to select up the correct value, since a largelearning rate can miss the optimal point, while a small learning ratecan slow the training process. For example, the gradient descentalgorithm can be used by the system 1 to generate the update of theweights values. To speed up the convergence of the iteration, resilientbackpropagation can e.g. be applied in the present case for treating theupdate of weight values differently depending on the derivative of theerror function. For optimization, larger alternative learning rate cane.g. be set for speeding up the iterations if the error gradient remainsin the same direction in neighboring time-steps and smaller alternativelearning rate when approaching the optimal weights.

Due to the total number data of measuring grid cells 1003 (resolution ofm×n), a single hidden layer can exceed 365 thousand elements. Totechnically optimize the storage requirement and the ANN structuretraining time, the geographic area 2 can e.g. be subdivided into 50×50cell-squared grids 1002, each grid 1002 having its own independent ANNstructure (the output layer having 1400 elements). It is to be notedthat with the same inventive technical approach, instead of measuringthe real-time discharges of the upstream catchments for the predictionof maximum flood inundation, rainfall measurements and/or flood levelmeasurements can be selected as input measuring values for themachine-learning-based structure. To optimize the ANN processingtechnically further, clustering can be applied to the training measuringdataset. Like this, the size of the training measuring dataset canoptimize and reduced while still keeping the main representative events.As such the training time can be reduced and the overfitting effectsminimized. Further, to measure the technical performance of theprediction processing of maximum flood inundation by the applied MLstructure, the mean squared error (MSE) of each grid can e.g. be used.It is assumed that the flood maps from the events used are the observedvalues. As each grid 1002 has its own independent training network, theMSE can e.g. be measured using all the grid cells 1003 in each grid1002.

The central measuring engine 10 comprises a flood threshold measurand1032, wherein the flood threshold measurand 1032 is selected from apercentage 23 of the geographic area 2 given by the area 21 measured asaffected to the total geographic area 2. The adaptive damage-coverstructure 1041 can e.g. only be triggered if the flood thresholdmeasurand 1032 is measured to exceed a predefined flood threshold value1033. In another embodiment variant, the adaptive damage-cover structure1041 can e.g. be triggered to provide a damage cover 1031 directlydependent on the measured flood threshold measurand 1032.

The central measuring engine 10 comprises an electronic flood trigger103 for triggering the adaptive damage-cover structure 1041 coveringphysical damages or loss associated with the measured occurrence of theflood event 4, the occurrence measurably impacting physical objects 3/31situated in the affected area 21 and/or grid cells 1011 measured asaffected 10111, wherein the adaptive damage-cover structure 1041 isadapted by the damage-cover system 104 based on the flood thresholdmeasurand 1032 providing the dynamic parametric flood impact cover 1031.The adaptive damage-cover structure 1041 of the damage-cover system 104can e.g. be triggered by transmitting electronic steering signals fromto the automatedly steered electronic first and/or secondresource-pooling system 1042/1043, wherein the adaptive damage-coverstructure 1041 is adapted by the damage-cover system 104 based on theflood threshold measurand 1032 transmitted by the electronic steeringsignals. Further, the triggering the adaptive damage-cover structure1041 for covering physical damages or loss 32 associated with themeasured occurrence of the flood event 4 can e.g. comprises generating aparametric coverage 1031 based on the adaptive damage-cover structure1041, and transferring, by an electronic payment transfer module, basedon the generated parametric coverage 1031 monetary pay-out parametervalues by electronic payment transfer to cover the physical damages orloss 32 associated with the measured occurrence. As an embodimentvariant, a premium value can e.g. automatically and optimized generatedbased on a specific selection of the parametric flood impact cover 1031.

The triggering the adaptive damage-cover structure 1041 for coveringphysical damages or loss 32 associated with the measured occurrence ofthe flood event 4 can e.g. comprise generating a parametric coverage1031 based on a cover activation signaling or electronic payout function1046, and transferring, by an electronic payment transfer module, basedon the output of the cover activation signaling or electronic payoutfunction 1046 a monetary pay-out by electronic payment transfer to coverthe physical damages or loss 32 associated with the measured occurrence.The cover activation signaling or electronic payout function 1046 cane.g. comprise a linear payout function 10461 and/or a stepped payoutfunction 10462 and/or a deductibles-based payout function 10463. Thecover activation signaling or electronic payout function 1046 can e.g.be selectable based on topography and/or exposure distribution. Thecover activation signaling or electronic payout function 1046 canfurther e.g. be user-specific definable.

FIG. 2 shows an exemplary schematic process of the present invention,which illustrates an overall operation of an embodiment of an automatedsystem providing a dynamic parametric flood impact cover for an objectphysically impacted by the flood and/or to an individual assigned to theimpacted object. An automated system 1, is disclosed for providing adynamic parametric flood impact cover to flood impact exposed physicalobjects 3 and/or a flood risk exposed individual in case of anoccurrence of a flood event 4. The dynamic parametric cover 1031 isprovided by using an adaptive risk-transfer structure 1041 based onphysical flood event measurements. In one embodiment, at referencenumber 51, a trigger is generated by a trigger module and the trigger isactivated upon occurrence of the flood event. The trigger may providemeasuring data 1021 corresponding to such as flood extent, flood depth.The generation and measurement of measuring data 1021 related to theflood event 4 is explained in detail in conjunction with FIG. 2 of thepresent invention. At reference number 52, an embodiment variant with apayout function 1046 is shown, where the payout function 1046 isactivated (at reference number 52 and triggers the payout calculationfunction (at reference number 53). The payout generated is dependent onoccurrence of the flood event, regardless of actual loss incurred in theflooding event. The payout function is explained in detail inconjunction with FIG. 3 of the present application. Further, in anotherembodiment, an independent third party system or an electronic reportingagent or broker or a third party calculation agent, at reference number54) determines an intensity of the flood event. The independent thirdparty/reporting agent provides the information to the object 3 orexposed individual (at reference number 54) to initiate payoutgeneration and automatically triggering payout transfer (at referencenumber 53) and also informs to the individual (at reference number 55).In another embodiment, an automated module may be used to generate thepayout, and automatically intimate the insured and initiate the payment.The exposed individual (at reference number 55) is the one who maypurchase a cover specifying a maximum payout from the system 1 (atreference number 56). The maximum payout cover is based on extend of theflooded area 21. The premium (at reference number 57) is transferred tothe system (at reference number 51) depends on the chosen limit as wellas on the area 21 affected by the flood 4 (at reference number 55). Inone embodiment, the premium is generated using probabilistic modellingstructures, upon numerous reviews.

In FIG. 3 , an exemplary structure scheme is shown presenting an impactof the flood event in a specific region. The trigger data may beobtained from highly reputed independent third party agency that aremutually in agreement with the individual. The third party agency maybe, for example, use on-air imaging device such as Synthetic ApertureRadar (SAR) satellite imagery and drone based imagery that providemultiple images to capture flood depth and flood extend.

In one embodiment, the automated system (at reference number 1) firstmeasures an Area of Interest (AOI). The AOI is an extended flooded areaof a total area cover of the region. The generated AOI is then dividedinto a plurality of grid cells (at reference number 1003 of a spatialgrid 1002. The grid cells 1003 defining the desired resolution, may bedetermined randomly or dynamically, for example, the grid cells may bedefined as two dimensional m×n blocks such as 500 m×500n (0.005×0.005deg.) blocks of approx. 8.72*10−5 radian. Upon occurrence of the floodevent, an affected area (AA) is determined, by calculating the number ofgrid cells that are totally flooded. In one embodiment, the grid cellsmeasured as flooded are determined using, for example, a neural networkapproach, a double grid approach, and the like. In addition, the gridcells measured as not flooded do not contribute to the measured affectedarea of the AOI. Thereafter, the percentage (at reference number 53 ofaffected area 21 is generated with the relation of AA/AOI. The payout isbased on the pre-defined trigger depending upon the percentage ofaffected area 21. It is to be noted that this predefined trigger canvary from risk-transfer to risk-transfer depending on specific userrequirements.

With respect to FIG. 4 , an exemplary payout structure 1046 can e.g. beused during occurrence of the flood event 2 in relation to topographyand exposure distribution is shown via graph. In one embodiment, thepayout function can be linear 10461 (straight linear), stepped or with10462 deductibles (franchise deductibles 10463). The automated systemselects payout functions based on topography and exposure distribution.The payout function 1046 may cater to different terrain and exposuredistribution with respect to a geographic area. The payout functionprovides different payout functions to the individual, and theindividual is free to select one of the payout function. The payoutfunction may further include suggestions from the individual orrisk-exposed insured.

For determining the payout scheme, a parametric coverage for themeasured affected area 21 may be generated by covering a possible lossassociated with occurrence of the flood event and impacting a geographicarea measured by the affected area. The possible loss incurred due tooccurrence of the flood event may be determined as per an adjustablerisk-transfer structure, a threshold measure. The possible loss incurredmay be triggered by a threshold-trigger that is selected from thepercentage of the affected area given by the measured affected area tothe geographic area (for example, the AOI). By way of an example, thegeographic area may include unvarying landscape that represents areacovered by dry land and wetland. In one exemplary embodiment, the payoutmay vary from 0% to 100%. As shown, the measured affected area is around75% and a limit of around USD 100 m is determined under the payoutscheme. The insured may receive around 0.75×USD 100 m or around USD 75m. In another embodiment, an electronic payment transfer may be made tothe individual by an electronic payment transfer module. The electronicpayment transfer may be done based on generated parametric coveragemonetary pay-out parameter values. The electronic payment transfer to bemade to the individual is limited and may be pre-defined based on priordiscussion or agreement with the insured. In some scenarios, theindividual may provide information related to the geographic area to beexcluded for coverage under the payout scheme.

In yet another embodiment, the risk-assessment parameters used todetermine occurrence of the flood event may be accessed using a machinelearning (ML) approach. The MI approach may connect flood relatedmeasurements of various geographic areas for assessing and/or predictingoverall risks by a risk transfer module. In addition, the MI approachmay be used to predict a future risk pertaining to occurrence of theflood event, and/or allowing to associate varied losses incurred in themultiple geographic areas due to occurrence of the flood event leadingto generation of risk factors for an associated reinsurance module.While a number of features are described herein with respect toembodiments of the inventions; features described with respect to agiven embodiment also may be employed in connection with otherembodiments. The following description and the annexed drawings setforth certain illustrative embodiments of the inventions. Theseembodiments are indicative, however, of but a few of the various ways inwhich the principles of the inventions may be employed.

LIST OF REFERENCES

-   -   1 Flood measuring and trigger system        -   10 Central measuring engine            -   100 Data aggregation and preprocessing module                -   1001 Dynamic grid splitter                -   1002 Topographic or geographic grid                -    10021, . . . , 1002 i Measuring parameters for grid                    cell i                -   1003 Measuring grid cells                -    10031 Grid cells measured as floated/affected                -    10032 Grid cells measured as not floated/affected                -    10033 Grid cell size                -    10034 Grid shape                -   1004 Persistence storage                -   1005 Predefined data structure                -    10051 Area parameter                -    100511 Geographic location                -    100512 Geographic extend                -   1006 Machine-learning based or Al-based module            -   102 Flood measuring devices/Flood measuring sensors                -   1021 Flood measuring parameters                -   1022 Loopback signaling                -   1023 Air-based optical measuring devices                -   1024 Space-based optical measuring devices                -   1025 Flood measuring gages with telemetry equipment                -   1026            -   103 Electronic flood trigger                -   1031 Dynamic parametric flood impact cover                -   1032 Flood threshold measurands                -   1033 Flood threshold value            -   104 Damage-signaling and/or damage-cover system                -   1041 Adaptive damage-cover structure                -   1042 First resource-pooling system                -    10421 First resources                -   1043 Second resource pooling system                -    10431 Second resources                -   1044 Electronic cover activation signaling and                    triggering                -   1045 Risk transfer parameter value optimization                -   1046 Cover activation function/Electronic payout                    function (signaling)                -    10461 Linear payout function                -    10462 Stepped payout function                -    10463 Payout function with deductibles (franchise                    deductibles)        -   11 Data Transmission network        -   12 Alarm signaling devices            -   121 Steering signaling to automated flood-alarm driven                devices            -   122 Acoustic alarm signal devices            -   123 Digital alarm messaging system    -   2 Geographic area        -   21 Affected area        -   22 Not affected area        -   23 Measured percentage value of affected area vs total            geographic area        -   24 Dry land area        -   25 Wet land area    -   3 Physical Objects in the geographic area (Flood exposed object)        -   31 Physical object in a grid cell        -   32 Physical damage or loss to the object impacted and caused            by the occurring flood event    -   4 Flood event        -   41 Temporal occurrence        -   42 Geographic/topographic location        -   43 Event Strength    -   5 Process Flow        -   51 Central measuring engine with cover activation signaling            and triggering        -   52 Damage cover activation by resource transfer/Payout            function        -   53 Payout parameter value generation and payout Trigger        -   54 Electronic reporting/Independent third party system        -   55 Flood exposed object/Risk exposed individual        -   56 Damage-cover system/Risk-transfer system        -   57 Automated pricing/Premium transfer

1. An automated method, implemented by a flood measuring and triggersystem, for providing a dynamic parametric flood impact cover forphysical objects being measurably impacted by an occurrence of a floodevent by using an adaptive damage-cover structure of a damage-coversystem based on physical flood event measurements, comprising:capturing, by a predefined data structure of a central measuring engine,a geographic area to be covered, the data structure at least comprisingdefinable area parameters capturing geographic location and/orgeographic extent of said geographic area; splitting, by the centralmeasuring engine, the geographic area to be covered by a spatial gridwith equidistant or adjusted grid cells of definable or adjustablydetermined size; measuring, by flood detection devices and/or floodsensors, an occurrence of a flood event by measuring floodings withinthe grid cells of the spatial grid, wherein flood measuring parametersof the flood detection devices and/or flood sensors are transmitted tothe central measuring engine and wherein, based on the transmitted floodmeasuring parameters, grid cells measured as flooded are contributing tothe area measured as affected while grid cells measured as not floodedare contributing to the area measured as not affected; measuring a floodthreshold measurand, wherein the flood threshold measurand is selectedfrom a percentage of the geographic area given by the area measured asaffected to the total geographic area; and triggering the adaptivedamage-cover structure to cover physical damages or losses associatedwith the measured occurrence of the flood event, the occurrencemeasurably impacting physical objects situated in the affected areaand/or grid cells measured as affected, wherein the adaptivedamage-cover structure is adapted by the damage-cover system based onthe flood threshold measurand providing the dynamic parametric floodimpact cover.
 2. The method according to claim 1, wherein the adaptivedamage-cover structure is only triggered if the flood thresholdmeasurand is measured to exceed a predefined flood threshold value. 3.The method according to claim 1, wherein the adaptive damage-coverstructure is triggered to provide a damage cover directly dependent onthe measured flood threshold measurand.
 4. The method according to claim1, wherein the adaptive damage-cover structure of the damage-coversystem is triggered by transmitting electronic steering signals from tothe automatedly steered electronic first and/or second resource-poolingsystem, wherein the adaptive damage-cover structure is adapted by thedamage-cover system based on the flood threshold measurand transmittedby the electronic steering signals.
 5. The method according to claim 1,wherein the triggering the adaptive damage-cover structure for coveringphysical damages or loss associated with the measured occurrence of theflood event comprises generating a parametric coverage based on theadaptive damage-cover structure, and transferring, by an electronicpayment transfer module, based on the generated parametric coveragemonetary pay-out parameter values by electronic payment transfer tocover the physical damages or loss associated with the measuredoccurrence.
 6. The method according to claim 1, wherein the grid cellsare defined as two dimensional blocks of m×n size.
 7. The methodaccording to claim 6, wherein the two dimensional m×n blocks are ofapproximately 8.72*10⁻⁵ radian size.
 8. The method according to claim 6,wherein the dimensional m×n blocks are of approximately 0.005*0.005 degsize.
 9. The method according to claim 1, wherein the geographic areacomprising surveyed landscape representative of area covered by dry landand wetland.
 10. The method according to claim 1, wherein the occurrenceof the flood event is detected using loopback signaling.
 11. The methodaccording to claim 1, wherein the grid cells measured as flooded aredetermined using at least an artificial intelligence baseddata-processing structure and/or a machine-learning-baseddata-processing structure.
 12. The method according to claim 1, whereinthe affected area is measured using at least partially air-based and/orspace-based optical measuring devices.
 13. The method according to claim1, further comprising generating a premium value based on a specificselection of the parametric flood impact cover.
 14. The method accordingto claim 1, wherein the triggering the adaptive damage-cover structurefor covering physical damages or loss associated with the measuredoccurrence of the flood event comprises generating a parametric coveragebased on a cover activation signaling or electronic payout function, andtransferring, by an electronic payment transfer module, based on theoutput of the cover activation signaling or electronic payout function amonetary pay-out by electronic payment transfer to cover the physicaldamages or loss associated with the measured occurrence.
 15. The methodaccording to claim 14, wherein the cover activation signaling orelectronic payout function comprises a linear payout function and/or astepped payout function and/or a deductibles-based payout function. 16.The method according to claim 14, wherein the cover activation signalingor electronic payout function is selectable based on topography and/orexposure distribution.
 17. The method according to claim 14, wherein thecover activation signaling or electronic payout function isuser-specific definable.
 18. The method according to claim 14, whereinthe geographic area comprises at least parts definable as automaticallyexcluded from the dynamic parametric flood impact cover.
 19. A floodmeasuring and trigger system for triggering a dynamic parametric floodimpact cover for physical objects being measurably impacted by anoccurrence of a flood event by using an adaptive damage-cover structureof an electronic damage-signaling and/or damage-cover system based onphysical flood event measurements, the flood measuring and triggersystem comprising: a central measuring engine with a predefined datastructure for capturing a geographic area to be covered, the datastructure at least comprising definable area parameters capturinggeographic location and/or geographic extent of said geographic area,wherein the predefined data structure comprises grid parameters forsplitting the geographic area to be covered by the spatial grid withequidistant or variable grid cells of definable or automaticallyadaptable size by means of a dynamic grid splitter, the flood measuringand trigger system comprises flood detection devices and/or floodsensors for measuring an occurrence of a flood event by measuringfloodings within grid cells of the spatial grid, wherein flood measuringparameters of the flood detection devices and/or flood sensors aretransmitted to the central measuring engine and wherein, based on thetransmitted flood measuring parameters, grid cells measured as floodedare contributing to the area measured as affected while grid cellsmeasured as not flooded are not contributing to the area measured asaffected; the central measuring engine comprises a flood thresholdmeasurand, wherein the flood threshold measurand is selected from apercentage of the geographic area given by the area measured as affectedto the total geographic area, and the central measuring engine comprisesan electronic flood trigger for triggering the adaptive damage-coverstructure covering physical damages or loss associated with the measuredoccurrence of the flood event, the occurrence measurably impactingphysical objects situated in the affected area and/or grid cellsmeasured as affected, wherein the adaptive damage-cover structure isadapted by the damage-cover system based on the flood thresholdmeasurand providing the dynamic parametric flood impact cover.